Robust matching-integrated vehicle rebalancing in ride-hailing system with uncertain demand
Name
Manuscript.pdf
Description
Submitted version
Size
1.51 MB
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Unknown
Checksum (MD5)
14c718b2491c5772053dacbe18f64b03
Author(s)
Guo, Xiaotong
Caros, Nicholas S
Zhao, Jinhua
Date Issued
August 2021
Journal
Transportation Research Part B: Methodological
Publisher
Elsevier BV
Citation
Guo, Xiaotong, Caros, Nicholas S and Zhao, Jinhua. 2021. "Robust matching-integrated vehicle rebalancing in ride-hailing system with uncertain demand." Transportation Research Part B: Methodological, 150.
Version
Original manuscript
Abstract
With the rapid growth of the mobility-on-demand (MoD) market in recent years, ride-hailing companies have become an important element of the urban mobility system. There are two critical components in the operations of ride-hailing companies: driver–customer matching and vehicle rebalancing. In most previous literature, each component is considered separately, and performances of vehicle rebalancing models rely on the accuracy of future demand predictions. To better immunize rebalancing decisions against demand uncertainty, a novel approach, the matching-integrated vehicle rebalancing (MIVR) model, is proposed in this paper to incorporate driver–customer matching into vehicle rebalancing problems to produce better rebalancing strategies. The MIVR model treats the driver–customer matching component at an aggregate level and minimizes a generalized cost including the total vehicle miles traveled (VMT) and the number of unsatisfied requests. For further protection against uncertainty, robust optimization (RO) techniques are introduced to construct a robust version of the MIVR model. Problem-specific uncertainty sets are designed for the robust MIVR model. The proposed MIVR model is tested against two benchmark vehicle rebalancing models using real ride-hailing demand and travel time data from New York City (NYC). The MIVR model is shown to have better performances by reducing customer wait times compared to benchmark models under most scenarios. In addition, the robust MIVR model produces better solutions by planning for demand uncertainty compared to the non-robust (nominal) MIVR model.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Massachusetts Institute of Technology. Department of Urban Studies and Planning
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/J.TRB.2021.05.015
